
"""simple docstring"""






import argparse
import os

import torch

from transformers import FlavaConfig, FlavaForPreTraining
from transformers.models.flava.convert_dalle_to_flava_codebook import convert_dalle_checkpoint




def __magic_name__	(				_lowerCamelCase:		int			)					->						List[Any]:

	'''simple docstring'''




	return sum(param.float().sum() if '''encoder.embeddings''' not in key else 0 for key, param in state_dict.items()			)




def __magic_name__	(				_lowerCamelCase:		List[str],					_lowerCamelCase:		str			)					->						int:

	'''simple docstring'''




	lowerCAmelCase												=  {}

	for key, value in state_dict.items():
		if "text_encoder.embeddings" in key or "image_encoder.embeddings" in key:
			continue

		lowerCAmelCase												=  key.replace('''heads.cmd.mim_head.cls.predictions''',					'''mmm_image_head'''			)
		lowerCAmelCase												=  key.replace('''heads.cmd.mlm_head.cls.predictions''',					'''mmm_text_head'''			)
		lowerCAmelCase												=  key.replace('''heads.cmd.itm_head.cls''',					'''itm_head'''			)
		lowerCAmelCase												=  key.replace('''heads.cmd.itm_head.pooler''',					'''itm_head.pooler'''			)
		lowerCAmelCase												=  key.replace('''heads.cmd.clip_head.logit_scale''',					'''flava.logit_scale'''			)
		lowerCAmelCase												=  key.replace('''heads.fairseq_mlm.cls.predictions''',					'''mlm_head'''			)
		lowerCAmelCase												=  key.replace('''heads.imagenet.mim_head.cls.predictions''',					'''mim_head'''			)
		lowerCAmelCase												=  key.replace('''mm_text_projection''',					'''flava.text_to_mm_projection'''			)
		lowerCAmelCase												=  key.replace('''mm_image_projection''',					'''flava.image_to_mm_projection'''			)
		lowerCAmelCase												=  key.replace('''image_encoder.module''',					'''flava.image_model'''			)
		lowerCAmelCase												=  key.replace('''text_encoder.module''',					'''flava.text_model'''			)
		lowerCAmelCase												=  key.replace('''mm_encoder.module.encoder.cls_token''',					'''flava.multimodal_model.cls_token'''			)
		lowerCAmelCase												=  key.replace('''mm_encoder.module''',					'''flava.multimodal_model'''			)
		lowerCAmelCase												=  key.replace('''text_projection''',					'''flava.text_projection'''			)
		lowerCAmelCase												=  key.replace('''image_projection''',					'''flava.image_projection'''			)

		lowerCAmelCase												=  value.float()

	for key, value in codebook_state_dict.items():
		lowerCAmelCase												=  value

	return upgrade




@torch.no_grad()
def __magic_name__	(				_lowerCamelCase:		List[Any],					_lowerCamelCase:		int,					_lowerCamelCase:		Union[str, Any],					_lowerCamelCase:		List[Any]=None			)					->						Optional[int]:

	'''simple docstring'''




	if config_path is not None:
		lowerCAmelCase												=  FlavaConfig.from_pretrained(_lowerCamelCase			)
	else:
		lowerCAmelCase												=  FlavaConfig()

	lowerCAmelCase												=  FlavaForPreTraining(_lowerCamelCase			).eval()

	lowerCAmelCase												=  convert_dalle_checkpoint(_lowerCamelCase,					_lowerCamelCase,					save_checkpoint=_lowerCamelCase			)

	if os.path.exists(_lowerCamelCase			):
		lowerCAmelCase												=  torch.load(_lowerCamelCase,					map_location='''cpu'''			)
	else:
		lowerCAmelCase												=  torch.hub.load_state_dict_from_url(_lowerCamelCase,					map_location='''cpu'''			)

	lowerCAmelCase												=  upgrade_state_dict(_lowerCamelCase,					_lowerCamelCase			)
	hf_model.load_state_dict(_lowerCamelCase			)
	lowerCAmelCase												=  hf_model.state_dict()
	lowerCAmelCase												=  count_parameters(_lowerCamelCase			)
	lowerCAmelCase												=  count_parameters(_lowerCamelCase			) + count_parameters(_lowerCamelCase			)

	assert torch.allclose(_lowerCamelCase,					_lowerCamelCase,					atol=1E-3			)

	hf_model.save_pretrained(_lowerCamelCase			)


if __name__ == "__main__":
					UpperCAmelCase        =				argparse.ArgumentParser()
					parser.add_argument("""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model.""")
					parser.add_argument("""--checkpoint_path""", default=None, type=str, help="""Path to flava checkpoint""")
					parser.add_argument("""--codebook_path""", default=None, type=str, help="""Path to flava codebook checkpoint""")
					parser.add_argument("""--config_path""", default=None, type=str, help="""Path to hf config.json of model to convert""")
					UpperCAmelCase        =				parser.parse_args()

					convert_flava_checkpoint(args.checkpoint_path, args.codebook_path, args.pytorch_dump_folder_path, args.config_path)




"""simple docstring"""






class 			lowercase					:






			def __init__(self  :	Dict			)       ->	List[Any]:





				"""simple docstring"""


				lowerCAmelCase												=  {}






			def 					UpperCAmelCase							(self  :	Union[str, Any]			)       ->	None:





				"""simple docstring"""


				print(self.vertex			)
				for i in self.vertex:
					print(SCREAMING_SNAKE_CASE_      ,''' -> '''      ,''' -> '''.join([str(SCREAMING_SNAKE_CASE_			) for j in self.vertex[i]]			)			)






			def 					UpperCAmelCase							(self  :	Optional[Any]      ,SCREAMING_SNAKE_CASE_  :	int      ,SCREAMING_SNAKE_CASE_  :	int			)       ->	None:





				"""simple docstring"""


				if from_vertex in self.vertex:
					self.vertex[from_vertex].append(SCREAMING_SNAKE_CASE_			)
				else:
					# else make a new vertex
					lowerCAmelCase												=  [to_vertex]






			def 					UpperCAmelCase							(self  :	List[str]			)       ->	None:





				"""simple docstring"""


				lowerCAmelCase												=  [False] * len(self.vertex			)

				# call the recursive helper function
				for i in range(len(self.vertex			)			):
					if not visited[i]:
						self.dfs_recursive(SCREAMING_SNAKE_CASE_      ,SCREAMING_SNAKE_CASE_			)






			def 					UpperCAmelCase							(self  :	Optional[Any]      ,SCREAMING_SNAKE_CASE_  :	int      ,SCREAMING_SNAKE_CASE_  :	list			)       ->	None:





				"""simple docstring"""


				lowerCAmelCase												=  True

				print(SCREAMING_SNAKE_CASE_      ,end=''' '''			)

				# Recur for all the vertices that are adjacent to this node
				for i in self.vertex:
					if not visited[i]:
						self.dfs_recursive(SCREAMING_SNAKE_CASE_      ,SCREAMING_SNAKE_CASE_			)


if __name__ == "__main__":
					UpperCAmelCase        =				Graph()
					g.add_edge(0, 1)
					g.add_edge(0, 2)
					g.add_edge(1, 2)
					g.add_edge(2, 0)
					g.add_edge(2, 3)
					g.add_edge(3, 3)

					g.print_graph()
					print("""DFS:""")
					g.dfs()

					# OUTPUT:
					# 0  ->  1 -> 2
					# 1  ->  2
					# 2  ->  0 -> 3
					# 3  ->  3
					# DFS:
					#  0 1 2 3


